Accelerating biopharmaceutical cell line selection with label-free multimodal nonlinear optical microscopy and machine learning
Abstract The selection of high-performing cell lines is crucial for biopharmaceutical production but is often time-consuming and labor-intensive. We investigated label-free multimodal nonlinear optical microscopy for non-perturbative profiling of biopharmaceutical cell lines based on their intrinsic...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Communications Biology |
| Online Access: | https://doi.org/10.1038/s42003-025-07596-w |
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| _version_ | 1823861767363624960 |
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| author | Jindou Shi Alexander Ho Corey E. Snyder Eric J. Chaney Janet E. Sorrells Aneesh Alex Remben Talaban Darold R. Spillman Marina Marjanovic Minh Doan Gary Finka Steve R. Hood Stephen A. Boppart |
| author_facet | Jindou Shi Alexander Ho Corey E. Snyder Eric J. Chaney Janet E. Sorrells Aneesh Alex Remben Talaban Darold R. Spillman Marina Marjanovic Minh Doan Gary Finka Steve R. Hood Stephen A. Boppart |
| author_sort | Jindou Shi |
| collection | DOAJ |
| description | Abstract The selection of high-performing cell lines is crucial for biopharmaceutical production but is often time-consuming and labor-intensive. We investigated label-free multimodal nonlinear optical microscopy for non-perturbative profiling of biopharmaceutical cell lines based on their intrinsic molecular contrast. Employing simultaneous label-free autofluorescence multiharmonic (SLAM) microscopy with fluorescence lifetime imaging microscopy (FLIM), we characterized Chinese hamster ovary (CHO) cell lines at early passages (0–2). A machine learning (ML)-assisted analysis pipeline leveraged high-dimensional information to classify single cells into their respective lines. Remarkably, the monoclonal cell line classifiers achieved balanced accuracies exceeding 96.8% as early as passage 2. Correlation features and FLIM modality played pivotal roles in early classification. This integrated optical bioimaging and machine learning approach presents a promising solution to expedite cell line selection process while ensuring identification of high-performing biopharmaceutical cell lines. The techniques have potential for broader single-cell characterization applications in stem cell research, immunology, cancer biology and beyond. |
| format | Article |
| id | doaj-art-eb5a3625730f4637af3b8f0eef4937fc |
| institution | Kabale University |
| issn | 2399-3642 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Biology |
| spelling | doaj-art-eb5a3625730f4637af3b8f0eef4937fc2025-02-09T12:50:46ZengNature PortfolioCommunications Biology2399-36422025-02-018111510.1038/s42003-025-07596-wAccelerating biopharmaceutical cell line selection with label-free multimodal nonlinear optical microscopy and machine learningJindou Shi0Alexander Ho1Corey E. Snyder2Eric J. Chaney3Janet E. Sorrells4Aneesh Alex5Remben Talaban6Darold R. Spillman7Marina Marjanovic8Minh Doan9Gary Finka10Steve R. Hood11Stephen A. Boppart12GSK Center for Optical Molecular Imaging, University of Illinois Urbana-ChampaignGSK Center for Optical Molecular Imaging, University of Illinois Urbana-ChampaignGSK Center for Optical Molecular Imaging, University of Illinois Urbana-ChampaignGSK Center for Optical Molecular Imaging, University of Illinois Urbana-ChampaignGSK Center for Optical Molecular Imaging, University of Illinois Urbana-ChampaignGSK Center for Optical Molecular Imaging, University of Illinois Urbana-ChampaignBiopharm Process Research, GlaxoSmithKlineGSK Center for Optical Molecular Imaging, University of Illinois Urbana-ChampaignGSK Center for Optical Molecular Imaging, University of Illinois Urbana-ChampaignPre-Clinical Sciences, Research, GlaxoSmithKlineBiopharm Process Research, GlaxoSmithKlineGSK Center for Optical Molecular Imaging, University of Illinois Urbana-ChampaignGSK Center for Optical Molecular Imaging, University of Illinois Urbana-ChampaignAbstract The selection of high-performing cell lines is crucial for biopharmaceutical production but is often time-consuming and labor-intensive. We investigated label-free multimodal nonlinear optical microscopy for non-perturbative profiling of biopharmaceutical cell lines based on their intrinsic molecular contrast. Employing simultaneous label-free autofluorescence multiharmonic (SLAM) microscopy with fluorescence lifetime imaging microscopy (FLIM), we characterized Chinese hamster ovary (CHO) cell lines at early passages (0–2). A machine learning (ML)-assisted analysis pipeline leveraged high-dimensional information to classify single cells into their respective lines. Remarkably, the monoclonal cell line classifiers achieved balanced accuracies exceeding 96.8% as early as passage 2. Correlation features and FLIM modality played pivotal roles in early classification. This integrated optical bioimaging and machine learning approach presents a promising solution to expedite cell line selection process while ensuring identification of high-performing biopharmaceutical cell lines. The techniques have potential for broader single-cell characterization applications in stem cell research, immunology, cancer biology and beyond.https://doi.org/10.1038/s42003-025-07596-w |
| spellingShingle | Jindou Shi Alexander Ho Corey E. Snyder Eric J. Chaney Janet E. Sorrells Aneesh Alex Remben Talaban Darold R. Spillman Marina Marjanovic Minh Doan Gary Finka Steve R. Hood Stephen A. Boppart Accelerating biopharmaceutical cell line selection with label-free multimodal nonlinear optical microscopy and machine learning Communications Biology |
| title | Accelerating biopharmaceutical cell line selection with label-free multimodal nonlinear optical microscopy and machine learning |
| title_full | Accelerating biopharmaceutical cell line selection with label-free multimodal nonlinear optical microscopy and machine learning |
| title_fullStr | Accelerating biopharmaceutical cell line selection with label-free multimodal nonlinear optical microscopy and machine learning |
| title_full_unstemmed | Accelerating biopharmaceutical cell line selection with label-free multimodal nonlinear optical microscopy and machine learning |
| title_short | Accelerating biopharmaceutical cell line selection with label-free multimodal nonlinear optical microscopy and machine learning |
| title_sort | accelerating biopharmaceutical cell line selection with label free multimodal nonlinear optical microscopy and machine learning |
| url | https://doi.org/10.1038/s42003-025-07596-w |
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